Missile-borne inertial navigation system aerial fine alignment method and system based on GNSS assistance

Abstract

The invention provides a missile-borne inertial navigation system aerial fine alignment method and system based on GNSS assistance, and relates to the technical field of integrated navigation, and the method comprises the following steps: obtaining system state data, position data and GNSS assisted navigation data of a missile-borne inertial navigation system, and constructing a measurement equation of the missile-borne inertial navigation system according to the system state data; determining a position error parameter and a system state error parameter according to the measurement equation and the data obtained in the step S1; inputting the data into a position prediction model for prediction to obtain a corresponding state prediction result and a position prediction result; and correcting the state prediction result and the position prediction result according to the state error parameter and the position error parameter of the system to complete the air fine alignment of the missile-borne inertial navigation system. By fusing multi-source data, considering an error model, constructing a decision model and the like, fine alignment in the air of the missile-borne inertial navigation system is realized, and the alignment precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated navigation, and more particularly to an airborne inertial navigation system air fine alignment method and system based on GNSS assistance. Background Art

[0002] At present, inertial navigation systems have the characteristics of complete autonomy, high concealment, high data update rate, etc., and play a very important role in modern precision guided bombs. For the strapdown inertial navigation system on the missile, high-precision initial alignment plays an important role in improving the navigation accuracy of the inertial navigation system.

[0003] However, traditional missile-borne inertial navigation systems need to perform a long static alignment process during ground startup to ensure the accuracy of their initial attitude and position information. However, during flight, due to complex and variable environmental conditions, such as the influence of factors such as dynamic acceleration and angular rate changes, traditional methods are difficult to provide sufficient accuracy, especially in long flight missions. In addition, traditional alignment methods usually rely on execution under static or near-static conditions, and have poor environmental adaptability.

[0004] Therefore, how to provide an airborne inertial navigation system air fine alignment method that can solve the above problems is an urgent problem for those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides an airborne inertial navigation system air fine alignment method and system based on GNSS assistance. By integrating multi-source data, considering error models, and constructing decision models, etc., air fine alignment of the missile-borne inertial navigation system is achieved, and the alignment accuracy is improved.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An airborne inertial navigation system air fine alignment method based on GNSS assistance, comprising the following steps:

[0008] S1: Obtain the system state data, position data, and GNSS-aided navigation data of the missile-borne inertial navigation system, and construct a measurement equation of the missile-borne inertial navigation system according to the system state data;

[0009] S2: Determine the position error parameter and the system state error parameter according to the measurement equation and the data obtained in S1;

[0010] S3: Construct a prediction model, and input the data obtained in S1 into the position prediction model for prediction to obtain corresponding state prediction results and position prediction results;

[0011] S4: Correct the state prediction result and position prediction result obtained in S4 according to the system state error parameter and position error parameter obtained in S2, and complete the in-air fine alignment of the missile-borne inertial navigation system.

[0012] Preferably, the system state data in S1 includes: misalignment angle data, velocity data, and acceleration data.

[0013] Preferably, S2 specifically includes:

[0014] S21: Construct a Kalman filter, input the misalignment angle data, velocity data, acceleration data, the position data, and the GNSS-aided navigation data into the Kalman filter for processing, and obtain the corresponding misalignment angle error parameter, velocity error parameter, and acceleration error parameter;

[0015] S22: Determine the corresponding position error data according to the position data and the GNSS-aided navigation data.

[0016] Preferably, S3 specifically includes:

[0017] S31: Obtain the historical system state data and device parameters of the missile-borne inertial navigation system, and preprocess the historical system state data, and form a corresponding data set with the device parameters as labels;

[0018] S32: Construct a prediction model, and divide the data set into a training set and a test set according to a preset ratio;

[0019] S33: Use the training set to train the prediction model, and at the same time use the test set to test the prediction model, calculate the model loss, and stop training when the model loss is the smallest;

[0020] S34: Input the data obtained in S1 into the trained prediction model for prediction, and obtain the corresponding state prediction result and position prediction result.

[0021] Preferably, S4 further includes:

[0022] Obtain real-time GNSS information to correct the corrected position prediction result again.

[0023] The present invention also provides a GNSS-aided in-air fine alignment system for a missile-borne inertial navigation system, including:

[0024] An acquisition module, configured to acquire the system state data, position data, and GNSS-aided navigation data of the missile-borne inertial navigation system, and construct a measurement equation of the missile-borne inertial navigation system according to the system state data;

[0025] An error determination module, configured to determine a position error parameter and a system state error parameter according to the measurement equation and the data obtained in S1;

[0026] A prediction module, configured to build a prediction model, and input the data obtained in S1 into the position prediction model for prediction, so as to obtain corresponding state prediction results and position prediction results;

[0027] A correction module, configured to correct the state prediction results and position prediction results obtained in S4 according to the system state error parameter and position error parameter obtained in S2, so as to complete the aerial fine alignment of the missile-borne inertial navigation system.

[0028] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method and system for aerial fine alignment of a missile-borne inertial navigation system based on GNSS assistance, and has the following beneficial effects:

[0029] (1) Improving the alignment accuracy: The present invention combines the actual attitude data and actual motion parameter data of the missile-borne inertial navigation system, and through the Kalman filter, the misalignment angle, speed, acceleration and GNSS data are fused in real time to accurately estimate the system error parameters (such as misalignment angle error, speed error, etc.). Through the fusion and processing of multi-source data, the alignment accuracy is improved;

[0030] (2) Optimizing the decision-making process: The present invention builds a prediction model and trains the prediction model (such as a machine learning model) using historical data, which can predict the system state and position in advance, reduce the real-time calculation delay, make the alignment process more intelligent and automated, and can adapt to different flight environments and mission requirements;

[0031] (3) The present invention realizes the aerial fine alignment of the missile-borne inertial navigation system by means of fusing multi-source data, building a dynamic simulation model, considering an error model and building a decision model, improves the alignment accuracy, optimizes the decision-making process and enhances the combat effectiveness, which is of great significance for improving the overall performance and combat effectiveness of the missile weapon system. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0033] Figure 1 It is the overall flowchart of a method for aerial fine alignment of a missile-borne inertial navigation system based on GNSS assistance provided by the present invention;

[0034] Figure 2 It is the timing diagram of the discrete inverse Kalman filtering algorithm provided by the embodiment of the present invention;

[0035] Figure 3 It is the structural principle block diagram of an air fine alignment system for a missile-borne inertial navigation system based on GNSS assistance provided by the present invention. Specific embodiments

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] See Figure 1 As shown, the embodiment of the present invention discloses an air fine alignment method for a missile-borne inertial navigation system based on GNSS assistance, including the following steps:

[0038] S1: Obtain the system state data, position data and GNSS-aided navigation data of the missile-borne inertial navigation system, and construct the measurement equation of the missile-borne inertial navigation system according to the system state data, where the specific expression of the measurement equation is:

[0039]

[0040] In the formula, H = [0 3×3 0 3×3 I 3×3 0 3×3 0 3×3 .

[0041] S2: Determine the position error parameter and the system state error parameter according to the measurement equation and the data obtained in S1;

[0042] S3: Construct a prediction model, and input the data obtained in S1 into the position prediction model for prediction to obtain the corresponding state prediction result and position prediction result;

[0043] S4: Correct the state prediction result and the position prediction result obtained in S4 according to the system state error parameter and the position error parameter obtained in S2 to complete the air fine alignment of the missile-borne inertial navigation system.

[0044] In a specific embodiment, the system state data in S1 includes: misalignment angle data, velocity data, and acceleration data.

[0045] In a specific embodiment, S2 specifically includes:

[0046] S21: Construct a Kalman filter, input the misalignment angle data, velocity data, acceleration data, the position data, and the GNSS-aided navigation data into the Kalman filter for processing to obtain corresponding misalignment angle error parameters, velocity error parameters, and acceleration error parameters. The Kalman filter can be a discrete inverse Kalman filtering algorithm. For the algorithm timing diagram, see Figure 2 as shown;

[0047] S22: Determine corresponding position error data according to the position data and the GNSS-aided navigation data.

[0048] Specifically, the discrete inverse Kalman filtering algorithm is summarized in Table 1 as follows.

[0049]

[0050]

[0051] For the convenience of calculation, in the discrete inverse Kalman filtering algorithm, the gyro measurement information and the angular velocity of the earth's rotation can be inverted, and the obtained relevant navigation results are used to solve F * (t k+1 ), and at the same time, the corresponding coefficient of the gyro constant drift ε * in F k+1 (t b ) should be taken as

[0052] In a specific embodiment, the S3 specifically includes:

[0053] S31: Obtain the historical system state data and device parameters of the missile-borne inertial navigation system, and preprocess the historical system state data, using the device parameters as labels to form a corresponding data set;

[0054] S32: Construct a prediction model, and divide the data set into a training set and a test set according to a preset ratio;

[0055] S33: Use the training set to train the prediction model, and at the same time use the test set to test the prediction model, calculate the model loss, and stop training when the model loss is minimized;

[0056] S34: Input the data obtained in the S1 into the trained prediction model for prediction to obtain corresponding state prediction results and position prediction results.

[0057] Specifically, the prediction model can include a filter combining cubature Kalman filter and particle filter and a model combining LSTM neural network, which can significantly improve the filtering performance.

[0058] In a specific embodiment, step S4 further includes:

[0059] Obtaining real-time GNSS information to further correct the corrected position prediction result.

[0060] See Figure 3 As shown, an embodiment of the present invention further provides a system using the method for airborne inertial navigation system air fine alignment based on GNSS assistance described in any one of the above embodiments, including:

[0061] An acquisition module, configured to acquire system state data, position data, and GNSS-aided navigation data of the airborne inertial navigation system, and construct a measurement equation of the airborne inertial navigation system according to the system state data;

[0062] An error determination module, configured to determine a position error parameter and a system state error parameter according to the measurement equation and the data obtained in step S1;

[0063] A prediction module, configured to construct a prediction model, and input the data obtained in step S1 into the position prediction model for prediction to obtain corresponding state prediction results and position prediction results;

[0064] A correction module, configured to correct the state prediction result and the position prediction result obtained in step S4 according to the system state error parameter and the position error parameter obtained in step S2, and complete the air fine alignment of the airborne inertial navigation system.

[0065] In order to fully compare the effects of the CKF algorithm and the velocity + attitude matching transfer alignment algorithm based on inverse Kalman filtering in the transfer alignment of large azimuth misalignment angles, 4 groups of experimental data were respectively verified by experiments according to the experimental methods adopted in this section, and the experimental estimation results are shown in Table 2 below.

[0066] Table 2 Comparison of Estimation Results of Transfer Alignment of Large Azimuth Misalignment Angles in Laboratory Semi-Physical Simulation

[0067]

[0068]

[0069] Note: The misalignment angle error in the table refers to the estimated value of the sub-inertial navigation misalignment angle by the RTS smoothing algorithm at the start time of navigation. The installation error angle error is obtained by comparing the installation error angle estimated by the CKF and the installation error angle estimated by the second fine alignment in the transfer alignment based on inverse Kalman filtering with the reference value.

[0070] It can be seen that in the large azimuth misalignment angle transfer alignment, the CKF method has poor estimation effects on the misalignment angle and installation error angle in the horizontal direction, while the velocity + attitude matching transfer alignment method based on the inverse Kalman filter can effectively estimate various errors of the slave inertial navigation, and is significantly superior to the CKF method in terms of estimation accuracy.

[0071] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same and similar parts between the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for related parts.

[0072] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An airborne inertial navigation system air fine alignment method based on GNSS assistance, characterized in that It includes the following steps: S1: Obtain the system state data, position data, and GNSS-aided navigation data of the missile-borne inertial navigation system, and construct a measurement equation for the missile-borne inertial navigation system according to the system state data; S2: Determine the position error parameter and the system state error parameter according to the measurement equation and the data obtained in S1; S3: Construct a prediction model, input the data obtained in S1 into the position prediction model for prediction, and obtain the corresponding state prediction result and position prediction result; S4: Correct the state prediction result and position prediction result obtained in S4 according to the system state error parameter and position error parameter obtained in S2, and complete the aerial fine alignment of the missile-borne inertial navigation system.

2. The air fine alignment method of a GNSS-aided missile-borne inertial navigation system according to claim 1, wherein The system state data in S1 includes: misalignment angle data, velocity data, and acceleration data.

3. A method for airborne precise alignment of a missile-borne inertial navigation system assisted by GNSS according to claim 2, characterized in that, S2 specifically includes: S21: Construct a Kalman filter, input the misalignment angle data, velocity data, acceleration data, the position data, and the GNSS-aided navigation data into the Kalman filter for processing, and obtain the corresponding misalignment angle error parameter, velocity error parameter, and acceleration error parameter; S22: Determine the corresponding position error data according to the position data and the GNSS-aided navigation data.

4. A method for airborne inertial navigation system air fine alignment based on GNSS assistance according to claim 3, characterized in that, S3 specifically includes: S31: Obtain the historical system state data and device parameters of the missile-borne inertial navigation system, preprocess the historical system state data, and form a corresponding data set with the device parameters as labels; S32: Construct a prediction model, and divide the data set into a training set and a test set according to a preset ratio; S33: Use the training set to train the prediction model, and at the same time use the test set to test the prediction model, calculate the model loss, and stop training when the model loss is minimized; S34: Input the data obtained in S1 into the trained prediction model for prediction, and obtain the corresponding state prediction result and position prediction result.

5. A method for airborne inertial navigation system air fine alignment based on GNSS assistance according to claim 2, characterized in that, S4 further includes: Obtain real-time GNSS information to correct the corrected position prediction result again.

6. A system using the method for airborne inertial navigation system air precision alignment based on GNSS assistance according to any one of claims 1-5, characterized in that, It includes: An acquisition module, configured to obtain the system state data, position data, and GNSS-aided navigation data of the missile-borne inertial navigation system, and construct a measurement equation for the missile-borne inertial navigation system according to the system state data; An error determination module, configured to determine the position error parameter and the system state error parameter according to the measurement equation and the data obtained in S1; A prediction module, configured to construct a prediction model, input the data obtained in S1 into the position prediction model for prediction, and obtain the corresponding state prediction result and position prediction result; A correction module, configured to correct the state prediction result and position prediction result obtained in S4 according to the system state error parameter and position error parameter obtained in S2, and complete the aerial fine alignment of the missile-borne inertial navigation system.

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